Assessing and monitoring agroenvironmental determinants of recreational freshwater quality using remote sensing

Diverse fecal and nonfecal bacterial contamination and nutrient sources (e.g. agriculture, human activities and wildlife) represent a considerable non-point source load entering natural recreational waters which may adversely affect water quality. Monitoring of natural recreational water microbial quality is most often based mainly on testing a set of microbiological indicators. The cost and labour involved in testing numerous water samples may be significant when a large number of sites must be monitored repetitively over time. In addition to water testing, ongoing monitoring of key environmental factors known to influence microbial contamination may be carried out as an additional component. Monitoring of environmental factors can now be performed using remote sensing technology which represents an increasingly recognized source of rigorous and recurrent data, especially when monitoring over a large or difficult to access territory is needed. To determine whether this technology could be useful in the context of recreational water monitoring, we evaluated a set of agroenvironmental determinants associated with fecal contamination of recreational waters through a multivariable logistic regression model built with data extracted from satellite imagery. We found that variables describing the proportions of land with agricultural and impervious surfaces, as derived from remote sensing observations, were statistically associated (odds ratio, OR = 11 and 5.2, respectively) with a higher level of fecal coliforms in lake waters in the southwestern region of Quebec, Canada. From a technical perspective, remote sensing may provide important added-value in the monitoring of microbial risk from recreational waters and further applications of this technology should be investigated to support public health risk assessments and environmental monitoring programs relating to water quality.

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Microbial risk classifications for recreational waters and applications to the Swan and Canning Rivers in Western Australia

Journal of Water and Health ◽

10.2166/wh.2011.016 ◽

2011 ◽

Vol 9 (1) ◽

pp. 70-79 ◽

Cited By ~ 3

Author(s):

B. Abbott ◽

R. Lugg ◽

B. Devine ◽

A. Cook ◽

P. Weinstein

Keyword(s):

Water Quality ◽

Risk Management ◽

Recreational Water ◽

Recreational Waters ◽

Two Phase ◽

Recreational Water Quality ◽

Body Contact ◽

Microbial Risk ◽

Risk Management Framework ◽

Department Of Health

Protecting recreational water quality where ‘whole-of-body contact’ activities occur is important from a public health and economic perspective. Numerous studies have demonstrated that infectious illnesses occur when swimming in faecally polluted waters. With the release of the 2008 Australian recreational water guidelines, the Western Australian (WA) Department of Health conducted a formal evaluation to highlight the advantages of applying the microbial risk management framework to 27 swimming beaches in the Swan and Canning Rivers in Perth, WA. This involved a two-phase approach: (i) calculation of 95th percentiles using historical enterococci data; and (ii) undertaking sanitary inspections. The outcomes were combined to assign provisional risk classifications for each site. The classifications are used to promote informed choices as a risk management strategy. The study indicates that the majority of swimming beaches in the Swan-Canning Rivers are classified as ‘very good’ to ‘good’ and are considered safe for swimming. The remaining sites were classified as ‘poor’, which is likely to be attributed to environmental influences. Information from the study was communicated to the public via a series of press releases and the Healthy Swimming website. The guidelines provide a sound approach to managing recreational water quality issues, but some limitations were identified.

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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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Physicochemical and Microbiological Water Quality Assessment of Aba Waterside River, Aba, Nigeria

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2019.01.0124 ◽

2019 ◽

Vol 3 (1) ◽

pp. 142-148

Author(s):

L. O. Bobor ◽

C. M. Umeh

Keyword(s):

Water Quality ◽

Drinking Water ◽

Management Strategies ◽

Oxygen Demand ◽

Water Quality Monitoring ◽

Control Measures ◽

Fecal Coliforms ◽

Water Quality Control ◽

Inorganic Substances ◽

The Impact

The indiscriminate disposal of industrial effluents and solid wastes in surface water bodies is detrimental to humans and aquatic organisms. Water quality monitoring is critical to identify pollutants of concern and develop effective management strategies. Hence, this study was conducted to assess the impact of waste disposal on the water quality of Aba Waterside River, Ogbor hill, Aba. Grab samples were collected upstream, midstream and downstream and some physicochemical and microbiological parameters were analyzed in accordance with standard methods for the analysis of water and wastewater. The results were compared with the Nigerian standard for drinking water quality and the national environmental effluent limitation regulations. Turbidity levels (10 -31mg/l) exceeded the maximum permissible levels for drinking water (5mg/l) and may be associated with higher levels of embedded disease-causing microbes and potentially harmful organic and inorganic substances. The biological oxygen demand midstream (1960mg/l) was remarkably high due to the effluent discharged from the abattoirs at that point. Fecal coliforms (3-198MPN/100ml) were detected in all samples, indicating the presence of other potentially harmful microorganisms. The findings of this study indicate that the water is unsuitable for direct drinking water purposes and stringent water quality control measures should be implemented.

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A review of research on application of remote sensing in lake water quality monitoring

10.1117/12.2624857 ◽

2021 ◽

Author(s):

Wenrui Han

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Lake Water ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Lake Water Quality ◽

Research On Application

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Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11141674 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1674 ◽

Cited By ~ 11

Author(s):

Fangling Pu ◽

Chujiang Ding ◽

Zeyi Chao ◽

Yue Yu ◽

Xin Xu

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Quality Data ◽

Inland Lakes ◽

Quality Classification ◽

Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

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Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

Remote Sensing ◽

10.3390/rs12101586 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1586

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

Rodrigo Sepúlveda ◽

Sergio I. Martinez-Martinez ◽

Markus Disse

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Temporal Variations ◽

Quality Parameters ◽

Parameters Estimation ◽

Learning Approaches ◽

Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

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